Discover How To Apply Machine Learning

Integrating the Machine Learning Mastery approach with WEKA was a very smart next step. Readers can now see practical applications of classification, regression, etc. in one of the most popular ML software programs.

I plan to use his book and one of the case studies/projects at the end of the book in an upcoming class. Jason excels in writing clearly which is extremely important if we want a more diverse group of users to learn Machine Learning

Bob HoytDirector of Health Informatics at the University of West Florida

Getting Started in Applied Machine Learning is Hard…it’s hard for more reasons than you even know

When you start out in applied machine learning, there is so much to learn.

For example:

There are the algorithms.

There is the data.

There is the specific problem you are working on.

There is the mathematics behind it all.

There is the tool you plan to use.

Often you need to learn a new programming language, like python or more esoteric languages like Matlab or R.

This does not have to be the case.

It is so much easier to learn one thing well, rather than try and possibly fail to learn a host of new things.

Get Past Overwhelm and Focus on Learning Just One Thing…how to deliver results using applied machine learning

The answer is to focus on one thing.

The one thing to learn when you are starting in machine learning is how to deliver a result.

That is, given a problem, how to work through it and deliver a set of predictions or how to deliver a model that can generate predictions.

Not just predictions, but accurate predictions that can be delivered robustly and reliably, that you can put your name or your company’s name against and in which you can feel confident.

You can learn how to deliver a result in applied machine learning by using a systematic process.

Learn the Process of Applied Machine Learning…the systematic process you can use to deliver results again and again

The systematic process of applied machine learning is the way you can learn how to deliver a result.

It is comprised of 5 steps that that you can use from beginning to end:

Defining your problem.

Preparing your data.

Evaluate a suite of algorithms.

Improve your results with tuning and ensembles.

Finalize your model and present results.

Here’s the trap that you will avoid by using a systemic process:

If you follow the advice on blogs and online course, you will end up spending months or years learning the intricate details of the math behind just a handful of machine learning algorithms, but you will have no idea about how to use these algorithms as a part of a much bigger predictive modeling project.

By jumping straight to the process, you skip years of frustration, waiting to learn how to these powerful tools into practice. You can start building models for real problems straight away, and come back to the details of how the algorithms work later, in the context of actually delivering a result.

Weka is the Best Platform for Practicing Applied Machine Learning
…because there is no code, no math, and the tool guides you through the process

The best tool to learn this process is the Weka machine learning workbench.

There are 3 main reasons why this is the case:

Speed: you can work through your problem fast, giving you more time to try lots of ideas.

Focus: it is just you and your problem, the tool gets out of your way.

Coverage: it provides lots of state-of-the-art algorithms to choose from.

It saves you from the cruft that you can encounter with other platforms.

You do not need to spend weeks learning a new language or API, and can focus on learning how to work through problems efficiently and effectively.

You can focus on the one valuable thing you need to learn: the process of applied machine learning and delivering a result. Later, you can learn how to use more and different tools.

You need to know how to map the tasks of an applied machine learning project onto the platform.

You need to know the best practices for working through each task in the process.

You also need to know strategies to practice and build a portfolio of completed projects that you can use to demonstrate your developing skills in applied machine learning.

Introducing the Ebook: Machine Learning Mastery With Weka

This Ebook was designed for you as a developer to rapidly get up to speed in applied machine learning using the Weka platform.

A step-by-step tutorial approach is used throughout the 18 lessons and 3 end-to-end projects, showing you exactly what to click and exactly what results to expect.

The goal is to get you using Weka to create your first models as quickly as possible, then guide you through the finer points of developing predictive models for classification and regression predictive modeling problems.

This Ebook is your guide to learning the in-demand skills you need to deliver results using applied machine learning on your own projects.

Let’s take a closer look at the breakdown of the lessons and projects you will discover inside this Ebook.

Everything You Need To Know to Develop Your Own Predictive Models

You Will Get:
18 Lessons on Applied Machine Learning With Weka
3 Project Tutorials that Tie it All Together

This ebook was written around two themes designed to get you started and using applied machine learning effectively and quickly.

These are Lessons and Projects:

Lessons: Learn how the subtasks of a applied machine learning project map onto the Weka platform and the best practice way of working through each task.

Projects: Tie together all of the knowledge from the lessons by working through case study predictive modeling problems.

Each project was designed to be completed by you in less than 60 minutes.

Machine Learning Mastery With Weka Table of Contents

Here’s Everything You’ll Get…
in Machine Learning Mastery With Weka

Hands-On Tutorials

A digital download that contains everything you need, including:

Clear descriptions that help you to understand the Weka platform for machine learning.

Step-by-step Weka tutorials to show you exactly how to apply each technique and algorithm.

End-to-end Weka projects that show you exactly how to tie the pieces together and get a result.

Digital Ebook in PDF format so that you can have the book open side-by-side with the tool and see exactly how each example works.

Gentle introduction to the platform and how to make best use of it, including:

The fact that applied machine learning is hard and how Weka can make it easy and even fun.

The Weka machine learning workbench and the 2 environments you must focus on using.

The benefit of a machine learning portfolio and how to demonstrate your growing skills in applied machine learning.

Foundation tutorials for getting started and data preparation, including:

The download and installation of Weka for each major platform (Windows, Linux and Mac)

The main interfaces of the Weka machine learning workbench, what they are for and what to expect.

The loading of data from CSV and ARFF formated files and the important foundation this lays for loading your own data.

The standard machine learning datasets and why they are so important when practicing in Weka.

The calculation of descriptive statistics and data visualization and why you must understand your data before modeling.

The scaling of data to meet the expectations of machine learning algorithms and the 2 most popular methods.

The powerful data transform capabilities of Weka and the 2 methods you will probably need on your problem.

The problem that missing data can have when modeling and how to fill in the gaps.

The requirement that some algorithms have to select the most important features in your data and 4 templates that you can copy.

Practical Projects

Lessons on applied machine learning with the Weka platform, including:

The large variety of machine learning algorithms offered in Weka and the 10 to focus on for best results.

The importance of estimating model performance on unseendata and 4 techniques you need to do so.

The need for estimating a baseline performance on a predictive modeling problem and how Weka makes that easy.

The necessity of not assuming a solution, the spot checking method and the linear and nonlinear algorithm recipes you can use immediately.

The improvement of results with ensemble methods and the 5 main techniques you can use on your projects.

The comparison and selection of trained models and the Experimenter interface that helps you choose.

The tuning of machine learning algorithm hyperparameters and the recipe you can use on any algorithm.

The finalization of a trained model to save it to file and later load it to make new predictions on unseen data.

Projects that tie together the lessons into end-to-end sequence to deliver a result, including:

The first machine learning project in Weka for multi-class classification that provides a gentle guide as to how the lessons tie together.

The binary classification problem that predicts the onset of diabetes showing the judicious use of data analysis and data preparation.

The regression project to predict house prices that shows the improvements of data transforms, tuning and ensemble methods.

Resources you need to go deeper, when you need to, including:

The best sources of information on the Weka platform, in case you are craving more information.

The best places online where you can ask your challenging questions and actually get a response.

What More Do You Need?

Take a Sneak Peek Inside The Ebook

About The Author

Hi, I'm Jason Brownlee.

I live in Australia with my wife and son and love to write and code.

I have a computer science background as well as a Masters and Ph.D. degree in Artificial Intelligence.

I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones. (yes I have written tons of code that runs operationally)

I get a lot of satisfaction helping developers get started and get really good at machine learning.

I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

I'm here to help if you ever have any questions. I want you to be awesome at machine learning.

Get Your Sample Chapter

Want to take a look at the Ebook? Download a free sample chapter PDF.

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Check Out What Customers Are Saying:

1. The content.
2. The presentation for a logical flow of insights.
3. Ease of understanding.
4. Hands-on exercises.
5. Keeping contact with his readers.

Great Stuff Jason Kind regards Jac

Jac SpiesManagement Consultant

Much more than just an intro for newcomers to ML. I’ve been using ML in R for years and found the book a far quicker way of getting started with Weka than by using the official Weka manual, plus nice examples.

Andy WebbManaging Director Omega Derivatives

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100% Money-Back Guarantee

If you're not happy with your purchase of any of the Machine Learning Mastery Ebooks,just email me within 90 days of buying, and I'll give you your money back ASAP.

Can I get an invoice for my purchase?

Email me with the details of your order (order number or email address used to make the purchase) and details you would like to appear on the invoice (your name, company name and address).

I will create a PDF invoice for you and email it back.

How long do books take to ship?

There are no physical books, therefore no shipping is required.

All books are EBooks that you can download immediately after you complete your purchase.

Do you ship to my country?

There are no physical books, therefore no shipping is required.

All books are EBooks that you can download immediately after you complete your purchase.

I support purchases from any country via PayPal or Credit Card.

Can I have a discount?

I do offer a discount to students, teachers, and retirees.

Note: I only offer discounts on individual books, not on the bundles. This is because the bundles are already heavily discounted.

If you are a student, teacher or a retiree please contact me and ask for the discount.

Do you have any sales, deals, or coupons?

No.

I generally don't do sales.

If I do have a special, such as around the launch of a new book, I only offer it to past customers and subscribers on my email list.

I do offer book bundles that offer a discount for a collection of related books.

Can I get a refund?

Yes.

I am sorry to hear that you want a refund.

Please contact me directly with your purchase details (order number or email address used to make the purchase) and I will organize a refund.

Will you help me if I have questions?

Yes.

Please contact me anytime with questions about machine learning or the books.

One question at a time please.

Also, each book has a final chapter on getting more help and further reading and points to resources that you can use to get more help.

Do I need to be a good programmer?

No.

Not at all.

My material requires that you have a programmers mindset of thinking in procedures and learning by doing.

You do not need to be an excellent programmer to read and learn about machine learning algorithms.

How much math do I need to know?

No background in statistics, probability or linear algebra is required.

I teach using a top-down and results-first approach to machine learning. You will learn by doing, not learn by theory.

There are no derivations.

Any questions presented are explained in full and are only provided to make the explanation clearer, not more confusing.

How much machine learning do I need to know?

Only a little.

If you are a reader of my blog posts, then you know enough to get started.

I do my best to lead you through what you need to know, step-by-step.

How long will the book take me to complete?

I recommend reading one chapter per day.

Some students finish the book in a weekend.

Most students finish the book in a few weeks by working through it during nights and weekends.

How are your books different to other books?

My books are playbooks. Not textbooks.

They have no deep explanations of theory, just working examples that are laser-focused on the information that you need to know to bring machine learning to your project.

My books are not for everyone, they are carefully designed for practitioners that need to get results, fast.

How are your books different from the blog?

The books are a concentrated and more convenient version of what I put on the blog.

I design my books to be a combination of lessons and projects to teach you how to use a specific machine learning tool or library and then apply it to real predictive modeling problems.

The books get updated with bug fixes, updates for API changes and the addition of new chapters, and these updates are totally free.

I do put some of the book chapters on the blog as examples, but they are not tied to the surrounding chapters or the narrative that a book offers and do not offer the standalone code files.

With each book, you also get all of the source code files used in the book that you can use as recipes to jump-start your own predictive modeling problems.

How are the 2 algorithms books different?

The book “Master Machine Learning Algorithms” is for programmers and non-programmers alike that learn through worked examples. It teaches you how 10 top machine learning algorithms work, with worked examples in arithmetic, not code (and spreadsheets) that show how each model learns and makes predictions.

The book “Machine Learning Algorithms From Scratch” is for programmers that learn by writing code to understand. It provides step-by-step tutorials on how to implement top algorithms as well as how to load data, evaluate models and more. It has less on how the algorithms work, instead focusing exclusively on how to implement each in code.

The two books can support each other.

Is there a team or company-wide license?

No.

Due to abuse of the privilege, I only support purchases by individuals.

Is there a license for libraries?

No.

Sorry, I only support purchases by individuals.

Do you have videos?

No.

I only have tutorial lessons and projects in text format.

This is by design. I used to have video content and I found the completion rate much lower.

I want you to put the material into practice. I have found that text-based tutorials are the best way of achieving this.

After reading and working through the tutorials you are far more likely to apply what you have learned.

What operating systems are supported?

Linux, Mac OS X and Windows.

Can you be my mentor or coach?

No.

Thanks for asking. I would love to help, but I just don't have the capacity.

I try to help as many people as possible through my blog and books.

Can I purchase from Amazon (or elsewhere)?

No.

My books can only be purchased from my website.

The reason is that I am a small business and I want a direct relationship with you, my customer, so that I can offer personal support and send out updates about your book and new stuff I am working on.

I hope you can understand my rationale.

What if my download link expires?

It is possible that your link to download your purchase will expire after a few days.

This is a security precaution.

Please contact me and I will resend you purchase receipt with an updated download link.

Can I use your code in my own project?

Yes.

But, understand that all code was developed and provided for educational purposes only and that I take no responsibility for it, what it might do or how you might use it.